Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST
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If you find this code useful in your research, please consider citing:
@inproceedings{zhong2020random, title={Random Erasing Data Augmentation}, author={Zhong, Zhun and Zheng, Liang and Kang, Guoliang and Li, Shaozi and Yang, Yi}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, year={2020} }
[Official Torchvision in Transform]
[Pytorch: Random Erasing for ImageNet]
[PersonreIDbaseline + Random Erasing + Re-ranking]
Requirements for Pytorch (see Pytorch installation instructions)
ResNet-20 baseline on CIFAR10:
python cifar.py --dataset cifar10 --arch resnet --depth 20
ResNet-20 + Random Erasing on CIFAR10:
python cifar.py --dataset cifar10 --arch resnet --depth 20 --p 0.5
ResNet-20 baseline on CIFAR100:
python cifar.py --dataset cifar100 --arch resnet --depth 20
ResNet-20 + Random Erasing on CIFAR100:
python cifar.py --dataset cifar100 --arch resnet --depth 20 --p 0.5
ResNet-20 baseline on Fashion-MNIST:
python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20
ResNet-20 + Random Erasing on Fashion-MNIST:
python fashionmnist.py --dataset fashionmnist --arch resnet --depth 20 --p 0.5
For ResNet:
--arch resnet --depth (20, 32, 44, 56, 110)
For WRN:
--arch wrn --depth 28 --widen-factor 10
You can reproduce the results in our paper:
| | CIFAR10 | CIFAR10| CIFAR100 | CIFAR100| Fashion-MNIST | Fashion-MNIST| | ----- | ----- | ---- | ----- | ---- | ----- | ---- | |Models | Base. | +RE | Base. | +RE | Base. | +RE | |ResNet-20 | 7.21 | 6.73 | 30.84 | 29.97 | 4.39 | 4.02 | |ResNet-32 | 6.41 | 5.66 | 28.50 | 27.18 | 4.16 | 3.80 | |ResNet-44 | 5.53 | 5.13 | 25.27 | 24.29 | 4.41 | 4.01 | |ResNet-56 | 5.31 | 4.89| 24.82 | 23.69 | 4.39 | 4.13 | |ResNet-110 | 5.10 | 4.61 | 23.73 | 22.10 | 4.40 | 4.01 | |WRN-28-10 | 3.80 | 3.08 | 18.49 | 17.73 | 4.01 | 3.65 |
If you have any questions about this code, please do not hesitate to contact us.